interpret_bf function

Interpret Bayes Factor (BF)

interpret_bf(
  bf,
  rules = "jeffreys1961",
  log = FALSE,
  include_value = FALSE,
  protect_ratio = TRUE,
  exact = TRUE
)

Arguments

• bf: Value or vector of Bayes factor (BF) values.
• rules: Can be "jeffreys1961" (default), "raftery1995" or custom set of rules() (for the absolute magnitude of evidence).
• log: Is the bf value log(bf)?
• include_value: Include the value in the output.
• protect_ratio: Should values smaller than 1 be represented as ratios?
• exact: Should very large or very small values be reported with a scientific format (e.g., 4.24e5), or as truncated values (as "> 1000" and "< 1/1000").

Details

Argument names can be partially matched.

Rules

Rules apply to BF as ratios, so BF of 10 is as extreme as a BF of 0.1 (1/10).

• Jeffreys (1961) ("jeffreys1961"; default)

  • BF = 1 - No evidence
  • 1 \< BF \<= 3 - Anecdotal
  • 3 \< BF \<= 10 - Moderate
  • 10 \< BF \<= 30 - Strong
  • 30 \< BF \<= 100 - Very strong
  • BF \> 100 - Extreme.

• Raftery (1995) ("raftery1995")

  • BF = 1 - No evidence
  • 1 \< BF \<= 3 - Weak
  • 3 \< BF \<= 20 - Positive
  • 20 \< BF \<= 150 - Strong
  • BF \> 150 - Very strong

Examples

interpret_bf(1)
interpret_bf(c(5, 2, 0.01))

References

• Jeffreys, H. (1961), Theory of Probability, 3rd ed., Oxford University Press, Oxford.
• Raftery, A. E. (1995). Bayesian model selection in social research. Sociological methodology, 25, 111-164.
• Jarosz, A. F., & Wiley, J. (2014). What are the odds? A practical guide to computing and reporting Bayes factors. The Journal of Problem Solving, 7(1), 2.